Vol.11, No.4, Oct.-Dec. 2017
ECTI e-magazine
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(“Acquiring Fundamental Traffic Variables with Mobile Sensors”)
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2 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
Message from
Dear Valued ECTI Members,
In this issue of ECTI E-Magazine, the last one in 2017, we are pleased to publish a review article titled
“Acquiring Fundamental Traffic Variables with Mobile Sensors” by Assoc.Prof. Dr. Sooksan Panichpapiboon (King
Mongkut’s Institute of Technology Ladkrabang). It describes recent research trends in intelligent transportation
system with the focus on availability and access of traffic variables today and the future.
Next year, a number of interesting conferences will be held, particularly, the ECTI-CON 2018 (Chiangrai),
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members as well as the public in this region and the world to register for these events. The conference gathering
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to come.
Pornchai Supnithi Watid Phakphisut
ECTI E-Magazine Editor ECTI E-Magazine Assistant Editor
Editor
3 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
Acquiring Fundamental Traffic Variables with Mobile Sensors
Sooksan Panichpapiboon
ABSTRACT
Fundamental traffic variables such as speed, density, and
flow are important for modeling the traffic dynamics. A
traffic model lets us understand how the traffic behaves and
helps us regulate it better. In addition, real-time traffic
information has become a crucial element of intelligent
transportation. With real-time traffic information, human
motorists as well as autonomous vehicles will be able to
select the most efficient routes to their destinations.
Acquiring accurate traffic data is the heart of any traffic
information system. Traditionally, raw traffic data are
obtained from fixed sensors that are installed in an
infrastructure-based traffic information system. These data
are then processed and transformed into meaningful traffic
variables such as speed, density, and flow. However, it is
foreseeable that vehicles will be used as mobile traffic
sensors in the near future. Consequently, what used to be
obtainable easily with fixed sensors in an infrastructure-
based system may no longer be obtainable when mobile
sensors are used. New methods for acquiring accurate
traffic variables with mobile sensors are required. In this
article, we discuss how the fundamental traffic variables are
typically obtained in the traditional fixed sensing system
and explore how they can be obtained in the new mobile
sensing paradigm.
Keywords
Traffic sensing; Vehicular sensing; Mobile sensors; Intelligent
transportation systems
I. INTRODUCTION
Real-time traffic information is an essential element of
intelligent transportation in a smart city. With the real-time
traffic information, human motorists as well as autonomous
vehicles will be able to select the most efficient routes to
their destinations. Moreover, it also allows dynamic
rerouting to be done wisely in the case that a traffic incident
occurs. The traffic information is not only resourceful to the
road users, but it is also valuable for the authorities.
Historical traffic data have been used extensively by the
authorities in transportation planning, design, and
operation.
Currently, many cities have their own traffic information
systems, which are capable of reporting up-to-date traffic
condition to the motorists. However, most of the current
traffic information systems are an infrastructure-based
system, where the traffic data are collected from fixed
sensors such as inductive loop detectors and surveillance
cameras. A typical infrastructure-based traffic information
system is shown in Fig. 1. It mainly consists of three parts: (i)
the sensors, (ii) the data collection center, and (iii) the
information outlets. The sensors are installed on the road to
collect the traffic data. The most commonly used sensors
are inductive loop detectors and surveillance cameras. The
traffic data collected by these sensors are then passed to
the data collection center for processing. Finally, the data
collection center processes the data and disseminates the
traffic information back to the motorists through various
kinds of information outlets, including traffic display boards,
websites, social media, etc.
Relying on fixed sensors, however, has many
shortcomings. First, the sensors need to be installed on the
road network. This usually takes a great deal of time and
efforts. For example, installing inductive loop detectors
would involve digging up the road surface. Second,
maintenance of these sensors in the system is troublesome.
Third, it is expensive to install fixed sensors to cover a large
area (e.g., city-wide or state-wide coverage). Finally, data in
the areas where there are no sensors installed cannot be
collected.
With an emerging new technology such as connected
vehicles (also known as vehicular ad hoc networks or
VANETs) [1], [2], a paradigm shift in traffic sensing is on its
verge. With this technology, vehicles will be able to act as
mobile sensors and collect the traffic data as they travel.
They may also disseminate and share the traffic information
with others. Hence, traffic sensing can be shifted from the
Figure 1: A typical infrastructure-based traffic information
system.
4 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
fixed sensing approach to a mobile sensing approach. A
mobile sensing system has many advantages over the
traditional infrastructure-based sensing system. First, it is a
lot more convenient to install sensors on a vehicle than on
a road. Sensors could be installed on a vehicle by the vehicle
manufacturer, or they could be installed later as add-ons by
the vehicle owner. Second, maintenance of these sensors
becomes much simpler. Sensors can be inspected regularly
when a vehicle is taken for its scheduled checkup. Finally,
and most importantly, vehicles can travel anywhere in the
road network; therefore, the traffic data can be collected
from everywhere.
In addition to the connected vehicles technology,
advances in the mobile devices technology also make
mobile sensing even more realizable. Mobile devices such
as smartphones and tablets are now equipped with a variety
of sensors such as global positioning system (GPS) receiver,
accelerometer, gyroscope, camera, and microphone. These
sensors can be exploited to collect traffic data. Moreover,
smartphones are now adopted by a large number of users.
Consequently, a person with a smartphone can turn any
vehicle, whether new or old, into a mobile traffic sensor. This
makes smartphones a great choice for mobile traffic
sensing devices.
Nonetheless, using vehicles as traffic sensors brings
many new challenges. Since the sensors now become
mobile and distributed, the methods and algorithms that
have been used for sensing, extracting, and processing the
traffic data in the traditional infrastructure-based sensing
system may no longer be applicable. There is a stark
difference between the types of measurements that a fixed
sensor and a mobile sensor make. Fixed sensors are
typically designed to collect spot measurements, which are
measurements at a specific observation point over time. For
example, an inductive loop detector collects a count of
vehicles at an observed location on a roadway over time. In
contrast, the mobile sensors move along with the traffic
stream and do not make measurements at a specific spot
over time. Moreover, the traffic variables that are easily
collectable by the fixed sensors might no longer be
obtainable easily with mobile sensors. For example, vehicle
counting can be done easily with a loop detector. However,
it is not trivial to count the number of vehicles passing an
intersection with a mobile sensor, especially in the practical
situation where only some of the vehicles on the road are
equipped with the sensors. Hence, the new ways of sensing
and extracting the traffic information will be needed.
The rest of this article is organized as follows. In Section
II we discuss how the fundamental traffic variables such as
speed, density, and flow are obtained in a traditional
infrastructure-based sensing system. In Section III, we
explore how these important traffic variables can be
obtained in a mobile sensing system. Finally, we conclude
this article in Section IV.
II. ACQUIRING TRAFFIC VARIABLES WITH FIXED
SENSORS
The three fundamental traffic variables that are used to
describe the macroscopic traffic dynamics are speed,
density, and flow. These three variables are related through
the well-known fundamental relation [3], which can be
written as
f = v (1)
where f is the traffic flow rate, is the traffic density, and v
is the space mean speed. In this section, we describe how
each of these fundamental traffic variables are obtained in
the traditional infrastructure-based sensing system.
A. Speed
The average speed is a macroscopic variable that measures
how fast the vehicles in the traffic stream move. It is typically
measured in a unit of m/s or km/h. In an infrastructure-
based sensing system, an inductive loop detector is usually
used to sense and collect the speed of a passing vehicle. An
inductive loop detector is a sensor which is implanted on a
road surface [4]. When an object (e.g., a vehicle) passes over
or lays on top of the loop detector, it changes the
inductance of the loop. The change in the inductance
indicates the presence of an object, and the knowledge of
the presence/absence of an object can be used for vehicle
counting. Furthermore, by using two inductive loop
detectors, the speed of a vehicle can be determined. In
order to determine the vehicle speed, two detectors will be
placed and separated by a known distance, denoted by d.
The time at which a vehicle passes the first detector,
denoted by t1, and the time at which it passes the second
detector, denoted by t2, will be recorded. The vehicle speed
can simply be obtained from d/(t2 - t1). The speed of each
passing vehicle can then be aggregated at the data
collection center, and the average speed of the vehicles
during a specified period can be calculated.
Many modern infrastructure-based sensing systems
use cameras as sensors for collecting the traffic data.
Typically, cameras will be installed highly above ground in
order to get an overlooking view of the traffic scene. A
video stream from each camera will be relayed to the data
collection center for processing. The speed of each vehicle
in the camera scene can be obtained through feature
tracking. By tracking the positions of the vehicles over
consecutive video frames, it is possible to determine the
distance that each vehicle moves per unit of time [5]–[8].
This allows the system to determine the speed of each
vehicle passing the camera as well as the average speed of
the vehicles in the traffic stream.
5 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
B. Density
Density is a traffic variable that measures the spatial
occupation of vehicles per unit road length. It basically
indicates how packed the vehicles are on the road. Density
is measured in a unit of vehicles per meter (veh/m). The
most direct way to measure the traffic density is by counting
the number of vehicles that are simultaneously present on
the observed road space. This may be done through aerial
photography. For example, a satellite image can provide an
aerial view of a road section, and thus the number of
vehicles on the section can be counted from the image.
However, aerial photography might not be practical,
especially for a long-term solution which regularly makes
measurements. As a result, traffic density is usually derived
indirectly from other traffic variables. In an infrastructure-
based sensing system that uses inductive loop detectors,
the traffic density can be derived indirectly from other traffic
variables collectable by them. In [9]–[11], traffic density is
derived from the speed and flow data collected by inductive
loop detectors. In [12], traffic density is estimated from the
ratio of the number of vehicles that moves between a pair
of loop detectors and the distance between them. In
addition, the traffic density can also be indirectly derived
from the percent occupancy, which is the percentage of
time that a point on a road is occupied by vehicles [3].
Intuitively, if a point on a road is occupied for a larger
fraction of time, it suggests that the traffic is denser. For
example, 100% occupancy implies that a point on the road
is always occupied, and this suggests that the traffic is
extremely dense.
In a traffic information system that uses video cameras
as sensors, the traffic density can be estimated from the
number of vehicles in the traffic scene. Basically, the
effective road area in the scene will be defined, and the
number of vehicles that are simultaneously present in the
area will be counted. There are a number of algorithms for
vehicle detection and counting [13]–[16]. The data collection
center can use these algorithms to process the video stream
and acquire the traffic density in the observed area.
C. Flow
Flow rate is a traffic variable that measures the number of
vehicles passing an observation point per unit of time. It has
a unit of vehicles per second (veh/s). It is straightforward to
determine a flow rate in an infrastructure-based system
because a vehicle count can be obtained easily from a fixed
sensor. With an inductive loop detector, a flow rate can be
determined effortlessly from the number of vehicles that
passes over the detector in a specified time period. With a
traffic camera, a flow rate can be determined from the
number of vehicles that passes the camera scene. Basically,
an image/video processing algorithm can be used to count
the number of vehicles crossing a reference point in the
traffic scene per unit of time. This idea is quite similar to that
of the inductive loop detector, and thus it is usually referred
to as the virtual loop detector. Image and video processing
techniques for vehicle identification, classification, and
counting can be found in [17]–[19].
III. ACQUIRING TRAFFIC VARIABLES WITH MOBILE
SENSORS
As the emerging technologies empower the vehicles to be
connected, mobile traffic sensing becomes increasingly
more appealing. In this section, we discuss how each of the
fundamental traffic variables could be obtained in a mobile
sensing manner.
A. Envisioned mobile sensing system
In mobile traffic sensing, some vehicles will act as sensors
and collect the traffic data. The traffic data will then be
passed to an online data collection center. These data will
then be processed and distributed back to the road users
through a variety of outlets such as websites, social media,
and mobile applications. In order to understand how each
type of the fundamental traffic variables can be collected
with mobile sensing, it is important to layout some basic
assumptions about the sensing system. Here are the
assumptions in an envisioned mobile traffic sensing
scenario.
• It is assumed that some of the vehicles will be equipped
with sensors and communication devices. These
vehicles will be referred to as sensing vehicles, which are
capable of collecting, transmitting, and receiving data.
Potentially, there are two types of technologies that can
facilitate this. The first candidate is the connected
vehicles technology. A typical connected vehicle will be
equipped with sensors such as GPS receiver, camera,
etc. In addition, a connected vehicle will be equipped
with an on-board unit (OBU), which allows it to
communicate with other vehicles through the vehicle-
to-vehicle (V2V) communication and allows it to
communicate with an infrastructure through the
vehicle-to-infrastructure (V2I) communication.
Moreover, many OBUs also have a cellular interface;
therefore, they will also be able to transmit and receive
data via a cellular network. The other potential
technology is the mobile devices technology. Mobile
devices such as smartphones are now equipped with a
variety of sensors, and they have become increasingly
more powerful. A smartphone can conveniently be
installed on a vehicle and used as a traffic sensing
device. Ultimately, regardless of the underlying
communication technology, each sensing vehicle will
be able to relay the traffic data to the data collection
center.
6 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
• Periodically, each sensing vehicle transmits the collected
data to the data collection center. The data collection
center then processes and disseminates the traffic
information to the public through the information outlets
• The overall general system architecture is shown in Fig. 2.
B. Speed
Speed is probably the easiest traffic variable collectable by
a mobile sensor. The most commonly used solution in
obtaining a vehicle speed is by using a GPS receiver. With a
GPS receiver, the position of a sensing vehicle can be
tracked and its speed can easily be acquired. Periodically,
each sensing vehicle sends its geolocation to the data
collection center. The data collection center uses these data
to estimate the speed of the sensing vehicle. With data from
multiple sensing vehicles, the average speed on a particular
road can be determined. Examples of mobile applications
that use positions of their users for speed sensing are
Google Maps and Waze [20]. Moreover, data do not have
to come from private vehicles only. Many traffic information
systems use public transport vehicles such as taxis and
busses as sensing vehicles [21]–[23]. The accuracy of the
estimated speed depends on the sampling rate of the GPS
data. Generally, the more frequently the vehicle position is
sampled, the more accurate the estimated speed is.
However, there is a tradeoff between accuracy and energy
consumption. A GPS receiver is a power-hungry sensor [24].
Using it continuously can quickly drain out the battery of
the mobile device. As a result, a mobile sensing approach
should also keep energy efficiency in mind.
There are several ways to reduce the energy
consumption incurred by the GPS receiver. One way is to
keep the sampling rate of the GPS receiver as low as
possible. Of course, reducing the sampling rate affects the
accuracy of the location and speed estimation [25].
Therefore, an appropriate sampling rate should be chosen
carefully. In [26], the authors propose an approach to
activate the GPS receiver when necessary. Basically, a GPS
receiver is switched on only if it is detected that the user is
in a “driving” mode. In this approach, different modes of
user activities such as driving, walking, and running are
classified using an accelerometer on a smartphone. By
shutting off the GPS receiver when a user is not in the
driving mode, the phone battery life can be prolonged.
Another way to be energy efficient is by using other
sensors that consume less power than the GPS receiver. In
order to reduce power consumption, a new approach in
determining the average speed of a sensing vehicle is
proposed in [27]. Instead of relying on the power-hungry
GPS receiver, the average speed is estimated from the data
sensed by an accelerometer, which consumes
approximately six times less power than the GPS receiver
[24]. In this approach, a smartphone is placed on each
sensing vehicle. The accelerometer on the smartphone is
then used to detect the state of the vehicle, whether it is
moving or stationary. Periodically, each sensing vehicle
sends a sequence of these states to the data collection
center. The average speed of a sensing vehicle can be
estimated from the sequence of its states. Basically, the
length of an interval that a vehicle can move continuously
is highly correlated with its average speed. Intuitively, if a
vehicle can move continuously for a long period of time, its
average speed tends to be high. On the contrary, if a vehicle
has to stop frequently during its trip, then its average speed
tends to be low. In [27], two methods for determining the
average speed of a vehicle from its sequence of states are
introduced. It is shown that they can achieve a satisfactory
level of accuracy.
C. Density
In comparison to the infrastructure-based sensing system,
it is more challenging to measure traffic density with mobile
sensors. Unlike the infrastructure-based traffic sensing
system, obtaining the number of vehicles that are
simultaneously present on a road with distributed mobile
sensors is not easy. One of the main reasons is that each
mobile sensor does not have an overlooking view of the
roadway (i.e., unlike having a fixed camera). Nonetheless,
there are a number of counting-based density estimation
approaches proposed in the literatures [28]–[32]. These
approaches are mainly designed for VANETs, and they
share the following common trait. Basically, if all vehicles are
equipped and can communicate with one another, then a
sensing vehicle will be able to check the number of its one-
hop neighbors. This allows a sensing vehicle to obtain the
number of vehicles within its transmission range and the
local density in its vicinity. The local density can further be
used to estimate the actual density on the road. In addition
to the number of neighboring vehicles, density can also be
estimated from the number of vehicles that form a
Figure 2: An example of a mobile traffic sensing system.
7 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
connected cluster [29]. Unfortunately, these approaches
only work under the assumption that all the vehicles on the
road are equipped with the communication devices, which
may be difficult to achieve in practice. Besides, if every
vehicle on the road is equipped, each one of them can
periodically send its location to the data collection center.
In this way, the data collection center knows exactly how
many vehicles are simultaneously present on a specific
road, and the actual density on the road can be determined.
However, it cannot be safely assumed that all the vehicles
will be equipped and that all of them will be willing to share
their location data. A practical density estimation approach
should work in a situation where only some of the vehicles
are willing to sense and share their traffic data.
There are also density estimation approaches that do
not rely on the assumption that all vehicles must be
equipped with the necessary sensing and communication
devices. These density estimation approaches have to
indirectly derived density from other quantities. In [33], a
local density is determined from the fraction of time that a
sensing vehicle stops during its trip. Basically, if the vehicle
has to stop frequently, it is implied that the traffic is dense.
In [34], the authors propose a density estimation approach
for a situation where there is a mix of connected vehicles
and ordinary vehicles. However, the density has to be
estimated indirectly from the speed of the sensing vehicles.
Basically, the speed is used to imply how dense the vehicles
are. In addition, it is also required that the average speed of
the connected vehicles and the average speed of the
ordinary vehicles are approximately the same.
Fortunately, in addition to counting, there is also
another possible way to straightly measure the traffic
density. In fact, the traffic density can be obtained from its
direct counterpart, the mean space headway. A space
headway is defined as the distance between the same
points on two successive vehicles. For example, a space
headway can be measured from a rear bumper of the
leading vehicle to the rear bumper of the following vehicle.
Intuitively, if the average space between each pair of
consecutive vehicles on the road is small, then it suggests
that the traffic is dense. In fact, the physical relation
between the traffic density and the mean space headway
can be described as [3]
= 1/E[X] (2)
where is the traffic density, X is a random variable
denoting the space headway between each pair of
consecutive vehicles, and E[X] is the mean of the space
headway. In other words, the traffic density can be
automatically obtained from the reciprocal of the mean
space headway. With the direct physical relation between
the traffic density and the space headway described in (2),
the traffic density can be determined by letting the sensing
vehicles measure their space headways and report these
measurements to the data collection center. With the space
headway samples from multiple sensing vehicles, the data
collection center will be able to estimate the mean space
headway and the traffic density. Thus, the key is to obtain
an accurate measurement of the space headway samples.
There are many types of sensors that a sensing vehicle can
use for estimating its space headway. In [35], an approach
to estimate a space headway with a smartphone is
introduced.
Assuming that each sensing vehicle can estimate a
space headway, it can periodically report this information
along with the geolocation and the time instant at which
the sample is taken to the data collection center. With these
space headway samples from multiple sensing vehicles, the
data collection center can compute the sample mean of the
space headway on a road at a specific point in time, and it
can use the computed sample mean to estimate the traffic
density with (2). More details on estimating the traffic
density with mobile sensors can be found in [35].
D. Flow
Ideally, if all of the vehicles on the road are equipped with
GPS receivers and communication devices, then the flow
rate can be determined easily. In this case, each vehicle can
report its location to the data collection center regularly.
Based on these GPS data, the data collection center can
acquire the flow rate at a particular location by counting the
number of vehicles that passes the location in a specified
time period. However, assuming that all vehicles can and
will cooperate in reporting their geolocations to the data
collection center is not practical.
One possible approach to determine the flow rate with
mobile sensors in a scenario where only some of the
vehicles are sensing vehicles is the following. Basically, each
sensing vehicle can collect their geolocation and the space
headway. As pointed out in the earlier sections, these two
types of data can be collected using a smartphone.
Periodically, each sensing vehicle can report these data to
the data collection center. Note that these data will allow
the data collection center to estimate the speed and the
density on a road. The flow rate can further be estimated
from the speed and the density using the fundamental
relation in (1). However, model calibration still requires
further investigation. In [36], the authors estimate the flow
rate from the theoretical speed-flow models. Basically, they
use the speed data obtained from probe vehicles and plug
them into four different speed-flow models, namely
Greenshield, Underwood, Northwestern, and Van Aerde, in
order to find out which model yields the best flow
estimation. The estimated flow rates are compared with the
ground truth values obtained from the loop detector data
collected on I-880 highway in San Francisco, California,
8 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
USA. It is shown that, among the four models, Van Aerde
model yields the best results. However, the accuracy of this
method is heavily affected by the validity of the applied
speed-flow model. In addition, the estimation method may
not work well in all traffic regimes.
IV. CONCLUDING REMARKS
Emerging technologies such as connected vehicles and
smart mobile devices will transform the way the traffic data
are collected and processed. These technologies empower
the vehicles to become mobile sensors. Connected vehicles
are anticipated to hit the road in the next few years. At least,
the new vehicles will likely have such a technology on-
board. As a result, many new vehicles will be able to help
contribute the traffic data as they travel. Nonetheless, it will
still take some time for the number of the connected
vehicles to reach a critical mass as traffic sensors. Thus, in
the beginning phase, connected vehicles will still be a small
fraction of the total vehicle population. In order to increase
the number of active mobile sensors and improve the
penetration rate, an approach to turn an ordinary vehicle
into a traffic sensor should also be considered.
Smart mobile devices such as smartphones, tablets, and
smartwatches are a great option for traffic sensing devices.
A smart mobile device can be placed on an ordinary vehicle
and can turn the vehicle into a mobile traffic sensor. A smart
device usually comes with a variety of built-in sensors.
These sensors can be exploited to sense the traffic data. The
key is to select an appropriate sensor for collecting the data
required to obtain the traffic variables of interest. Smart
mobile devices, especially smartphones, are adopted by a
large number of users. Thus, if used as traffic sensing
devices, they can help boost the number of mobile traffic
sensors tremendously.
Finally, since the types of raw data that the mobile
sensors can collect may be different from those obtained
by the fixed sensors, new approaches are required to
process and transform these data into meaningful traffic
variables. In this article, we have discussed a variety of ways
to acquire the three fundamental traffic variables, namely
speed, density, and flow, from the data collected by the
mobile sensors. For speed estimation, it is most convenient
to use the GPS data collected from the mobile sensors.
However, it should be kept in mind that a GPS receiver
consumes a lot of power. As a result, it can drain out the
device battery quickly. An alternative solution for speed
estimation is to use an accelerometer which consumes
much less power [27]. For traffic density, it is possible to
determine the density from the reciprocal of the mean
space headway [35]. Flow is, perhaps, the most difficult
variable to acquire from the mobile sensors. However,
assuming that speed and density are obtained, the flow rate
could be estimated from the fundamental relation.
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[22] P.-J. Tseng, C.-C. Hung, Y.-H. Chuang, K. Kao, W.-H. Chen,
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BIOGRAPHY
Dr. Sooksan Panichpapiboon received the
B.S., M.S., and Ph.D. degrees from Carnegie
Mellon University, Pittsburgh, PA, USA, in
2000, 2002, and 2006, respectively, all in
electrical and computer engineering. In April
2008, he was a Visiting Researcher with the
Department of Information Engineering,
University of Parma, Parma, Italy. He is currently an Associate
Professor with the Faculty of Information Technology, King
Mongkut’s Institute of Technology Ladkrabang, Bangkok,
Thailand. His current research interests include intelligent
transportation systems, vehicular ad hoc networks, mobile sensors,
and performance modeling.
Dr. Panichpapiboon was a recipient of the ASEM DUO-
Thailand Fellowship in 2007. He received the Doctoral Dissertation
Award from the National Research Council of Thailand in 2011. He
received the Siew Karnchanachari Award for Electrical Engineering
in 2015. He is a Senior Member of the IEEE.
10 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
More About the Authors…
Sooksan Panichpapiboon
Title: Associate Professor
Senior Member, IEEE
Office: Faculty of Information Technology
King Mongkut’s Institute of Technology Ladkrabang
1 Soi Chalongkrung 1, Ladkrabang, Bangkok, Thailand
E-mail: [email protected]
URL: http://www.it.kmitl.ac.th/~sooksan/
• Ph.D. in Electrical and Computer Engineering • Intelligent Transportation Systems
Carnegie Mellon University, Pittsburgh PA, USA • Vehicular Ad Hoc Networks
• Wireless Sensor Networks
1. S. Panichpapiboon and P. Leakkaw, “Traffic Density Estimation: A Mobile Sensing Approach,” IEEE
Communications Magazine, Dec. 2017, to appear.
2. S. Panichpapiboon and P. Leakkaw, “Traffic Sensing Through Accelerometers,” IEEE Transactions on
Vehicular Technology, vol. 65, no. 5, pp. 3559-3567, May 2016.
3. S. Panichpapiboon, “Time-Headway Distributions on an Expressway: Case of Bangkok,” ASCE Journal of
Transportation Engineering, vol. 141, no. 1, pp. 05014007, Jan. 2015.
4. S. Panichpapiboon and L. Cheng, “Irresponsible Forwarding Under Real Inter-vehicle Spacing Distributions,”
IEEE Transactions on Vehicular Technology, vol. 62, no. 5, pp. 2264-2272, June 2013.
5. L. Cheng and S. Panichpapiboon, “Effects of Intervehicle Spacing Distributions on Connectivity of VANET: A
Case Study from Measured Highway Traffic,” IEEE Communications Magazine, vol. 50, no. 10, pp. 90-97, Oct.
2012.
6. S. Panichpapiboon and W. Pattara-atikom, “A Review of Information Dissemination Protocols for Vehicular
Ad Hoc Networks,” IEEE Communications Surveys and Tutorials, vol. 14, no. 3, pp. 784-798, Third Quarter,
2012.
7. S. Panichpapiboon and W. Pattara-atikom, “Exploiting Wireless Communication in Vehicle Density
Estimation,” IEEE Transactions on Vehicular Technology, vol. 60, no. 6, pp. 2742-2751, July 2011.
8. S. Panichpapiboon, G. Ferrari, and O. K. Tonguz, “Connectivity of Ad Hoc Wireless Networks: An Alternative
to Graph-Theoretic Approaches,” Wireless Networks, vol. 16, no. 3, pp. 793-811, Apr. 2010.
Education Research Interests
List of Selected Publications
11 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
ECTI-EEC Transaction: -
Website: http://www.ecti-eec.org/index.php/ecti-eec
Two issues are available annually. The next issue will be available soon.
ECTI-CIT Transaction: -
Website: https://www.tci-thaijo.org/index.php/ecticit
Two issues are available annually. The next issue will be available soon.
Paper List of ECTI Transaction
12 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
Report from Conferences/Workshops/Seminars/Events
ECTI-ICROS Joint Session on Advances of Control System Design @ ICCAS 2017
Date: Oct. 19, 2017
Venue: 17th International Conference on Control, Automation and Systems
Phuket Graceland Resort & Spa, Phuket, Thailand
Main Organizer: Prof. Dr. David Banjerdpongchai
6th Joint Seminar on Control Systems
(JSCS 2017)
Date: Nov. 24, 2017
Venue: Faculty of Engineering, KMUTNB
Main Organizer: Asst. Prof. Dr. Chirdpong Deelertpaiboon
13 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
ISAP 2017
Date: Oct. 30 – Nov. 2, 2017
Venue: Phuket Graceland Resort & Spa, Phuket, Thailand
Submitted Papers: 452 (63 papers from Thailand)
Accepted Papers: 411
Website: http://www.isap2017.org/index.php
14 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
STRI’s Lecture Series
Date: Nov. 6, 2017
Venue: STRI, KMUTNB
Speaker: Prof. Prabhakar Pathak, USF, USA
15 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
IEEE ComSoc Distinguished Lecture Tour
Date: Dec. 25, 2017
Venue: Telecommunication Engineering Department, KMITL
Topic: Ambient Backscatter Assisted Wireless Powered Communications
Speaker: Prof. Dusit Niyato, NTU, Singapore
16 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
Announcements/Upcoming events/Call-for-Papers
17 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
18 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
19 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
20 ECTI E-magazine Vol.11, No.4, Oct.-Dec. 2017
ECTI Who’s Who
ECTI President
Somsak Choomchuay (KMITL)
ECTI Vice President
Kosin Chamnongthai (KMUTT)
Pinit Srithorn (RMUTI)
Advisory Board
Sawasd Tantaratana
Wanlop Surakampontor
Booncharoen Sirinaovakul
Monai Krairiksh (KMITL)
Prabhas Chongstitvatana (CU)
Prayoot Akkaraekthalin (KMUTNB)
Board Committee
Wannaree Wongtrairat (RMUTI)
Wiboon Promphanich (KMITL)
Panuwat Janpugdee (CU)
Akkarat Boonpoonga (KMUTNB)
Kittisak Phaebua (KMUTNB)
Theerayod Wiangtong (MUT)
Rangsan Wongsan (SUT)
ECTI Journal Editor
EEC: Apisak Worapishet (MUT)
CIT: Prabhas Chongstitvatana (CU)
Kosin Chamnongthai (KMUTT)
Technical Chair (TC)
TC (Electrical Engineering)
Nattachote Rugthaicharoencheep (RMUTPK)
TC (Electronics)
Sataporn Pornpromlikit (KKU)
TC (Electromagnetics)
Danai Torrungrueng (AsianU)
TC (Computers)
Krerk Piromsopa (CU)
TC (Telecommunications)
Keattisak Sripimanwat
TC (Information Technology)
Pratya Nuankaew (UP)
Regional Committee
Thailand: North: Roungsan Chaisricharoen (MFU)
Thailand: South: Petcharat Suriyachai (PSU)
Myanmar: Saya Oo (YTU)
Laos: Somsanouk Phatumvanh (NUOL)
Cambodia: Des Phal (RUPP)
ECTI E-Magazine Editorial Board
Monai Krairiksh (KMITL)
Jirayuth Mahhattanakul (MUT)
Prayoot Akkaraekthalin (KMUTNB)
Apisak Worapishet (MUT)
Editorial Team
Editor: Pornchai Supnithi (KMITL)
Assistant Editor: Watid Phakphisut (KMITL)
Secretary
Pairin Kaewkuay
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